code: def validate(
      model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=""
  ):
      batch_time_m = AverageMeter()
      losses_m = AverageMeter()
      top1_m = AverageMeter()
      top5_m = AverageMeter()
  
      model.eval()
  
      end = time.time()
      last_idx = len(loader) - 1
      with torch.no_grad():
          for batch_idx, (input, target) in enumerate(loader):
              last_batch = batch_idx == last_idx
              if not args.prefetcher:
                  input = input.cuda()
                  target = target.cuda()
              if args.channels_last:
                  input = input.contiguous(
                      memory_format=torch.channels_last
                  )
  
              with amp_autocast():
                  output = model(input)
              if isinstance(output, (tuple, list)):
                  output = output[0]
  
              # augmentation reduction
              reduce_factor = args.tta
              if reduce_factor > 1:
                  output = output.unfold(
                      0, reduce_factor, reduce_factor
                  ).mean(dim=2)
                  target = target[0 : target.size(0) : reduce_factor]
  
              loss = loss_fn(output, target)
              acc1, acc5 = accuracy(output, target, topk=(1, 5))
  
              if args.distributed:
                  reduced_loss = reduce_tensor(
                      loss.data, args.world_size
                  )
                  acc1 = reduce_tensor(acc1, args.world_size)
                  acc5 = reduce_tensor(acc5, args.world_size)
              else:
                  reduced_loss = loss.data
  
              torch.cuda.synchronize()
  
              losses_m.update(reduced_loss.item(), input.size(0))
              top1_m.update(acc1.item(), output.size(0))
              top5_m.update(acc5.item(), output.size(0))
  
              batch_time_m.update(time.time() - end)
              end = time.time()
              if args.local_rank == 0 and (
                  last_batch or batch_idx % args.log_interval == 0
              ):
                  log_name = "Test" + log_suffix
                  _logger.info(
                      "{0}: [{1:>4d}/{2}]  "
                      "Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  "
                      "Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  "
                      "Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  "
                      "Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})".format(
                          log_name,
                          batch_idx,
                          last_idx,
                          batch_time=batch_time_m,
                          loss=losses_m,
                          top1=top1_m,
                          top5=top5_m,
                      )
                  )
  
      metrics = OrderedDict(
          [
              ("loss", losses_m.avg),
              ("top1", top1_m.avg),
              ("top5", top5_m.avg),
          ]
      )
  
      return metrics
  
imports: import argparse
  import time
  import yaml
  import os
  import glob
  import math
  import logging
  from collections import OrderedDict
  from contextlib import suppress
  from datetime import datetime
  import torch
  import torch.nn
  import torchvision.utils
  from torch.nn.parallel import DistributedDataParallel
  from timm.data import create_dataset, create_loader, resolve_data_config, Mixup, FastCollateMixup, AugMixDataset
  from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint, model_parameters
  from timm.layers import convert_splitbn_model
  from timm.utils import *
  from timm.loss import *
  from timm.optim import create_optimizer_v2, optimizer_kwargs
  from timm.scheduler import create_scheduler
  from timm.utils import ApexScaler, NativeScaler
  import models
  from misc.distillation_loss import DistillationLoss
  from misc.cosine_annealing import CosineWDSchedule
label_desc: Use image node_id 4 for calling a global function with example usage: validate(model, loader, loss_fn, args, amp_autocast, log_suffix) and returns: metrics